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Dystonia is an abnormal involuntary movement or posture owing to sustained or intermittent muscle contraction. Standard treatment for dystonia includes medications, such as levodopa, anticholinergic and antiepileptic drugs, botulinum toxin, and baclofen pump, and surgeries, such as lesioning surgery and deep-brain stimulation. New treatment modalities aimed toward improving dystonia care in the future are under investigation. There are two main axes to improve dystonia care; one is non-invasive neuromodulation, such as transcranial magnetic stimulation, transcranial electrical stimulation, and transcutaneous electrical nerve stimulation. The other is a quantitative evaluation of dystonia using a wearable device and motion-capturing system, which can be empowered by artificial intelligence. In this article, the current status of these axes will be reviewed.

Cognitive decline in Parkinson’s disease (PD) significantly impacts patients’ quality of life, yet early detection remains challenging. While structural brain abnormalities in cortical regions have been widely documented using magnetic resonance imaging (MRI), subcortical regions have received less analytical attention despite their potential role as early biomarkers. This study investigated changes in specific subregions of the amygdala, thalamus, and hypothalamus in patients with PD before cognitive decline development. We analyzed MRI data from 163 participants (97 healthy controls [HC] and 66 patients with PD) from the Parkinson’s Progression Markers Initiative database. The patients with PD were classified based on cognitive status during a four-year follow-up: 21 who developed cognitive impairment (PDCI) and 45 who maintained normal cognition (PDNC). Cognitive function was assessed using the Montreal Cognitive Assessment and domain-specific tests. The PDCI group showed significantly lower corrected brain volumes in specific subregions of the amygdala (left basal nucleus), thalamus (bilateral lateral geniculate nuclei, right medial dorsal nucleus, and right anterior pulvinar nucleus), and hypothalamus (bilateral anterior-superior and left superior tubular parts) compared to that of HC. A significant difference between the PDCI and PDNC groups was observed only in the left lateral geniculate nucleus. In contrast, widespread structural changes were observed in cortical regions in the PDCI group, which showed stronger correlations with memory, attention, executive function, and visuospatial abilities. Hazard ratio analysis confirmed that structural changes in multiple cortical regions were significant predictors of cognitive decline. Although structural alterations were observed in subcortical regions, cortical changes demonstrated stronger associations with cognitive decline. These findings suggest that structural abnormalities may appear in the cerebral cortex before the stage proposed by conventional α-synuclein propagation models, potentially involving multiple mechanisms beyond α-synuclein, including global neural circuit dysfunction, disruption of neurotransmitter systems, breakdown of compensatory mechanisms, and coexisting pathologies (beta-amyloid and tau proteins). This study provides insights into early brain changes in PD and emphasizes the need for a comprehensive approach considering multiple mechanisms in early diagnosis and intervention strategies for PD-related cognitive impairment.

We investigated and characterized the prevalence of dry eye disease (DED) in Parkinson’s disease (PD). PubMed and EMBASE databases were searched for relevant studies between January 1, 1979 and March 10, 2022. Quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Study-specific estimates were combined using the DerSimonian–Laird random-effects model. Prevalence of subjective DED symptoms in patients with PD and mean differences in blink rate, corneal thickness, tear film breakup time, and tear secretion volume on Schirmer test I were compared to those in controls. Of 383 studies, 13 (1519 patients with PD) and 12 were included in qualitative and quantitative syntheses, respectively. Meta-analysis revealed a 61.1% prevalence of subjective DED symptoms in PD and that, compared with controls, patients with PD had significantly lower blink rate, thinner corneal thickness, shorter tear film breakup time, and lower tear secretion volumes on Schirmer test I, without and with anesthesia.

Parkinsonism-dominant multiple system atrophy (MSA-P) is typically a progressive disorder with poor responsiveness to levodopa and an unfavorable prognosis. However, in certain cases, the response to levodopa can be as robust as in Parkinson’s disease (PD), with severe motor fluctuations developing during treatment. Unlike PD, no established therapy exists to maintain activities of daily living (ADLs) in such patients. We present three cases of young-onset MSA-P who demonstrated sustained levodopa responsiveness and were treated with levodopa–carbidopa intestinal gel (LCIG) following the emergence of disabling motor fluctuations. In all three patients, parkinsonism was the predominant symptom from onset until LCIG initiation, with only mild autonomic or cerebellar symptoms. Prior to LCIG introduction, their motor complications closely resembled those of advanced PD. LCIG therapy successfully reduced “off” time and dyskinesia in all cases. However, long-term follow-up revealed a gradual decline in ADLs due to disease progression. These cases suggest that LCIG may be a valuable treatment option for selected MSA-P patients with preserved levodopa responsiveness.

Understanding perception and communication gaps between patients with neurological diseases and their treating physicians is essential for optimizing patient-centered care. The GAP-AI study aimed to identify these gaps in a cohort of patients with Parkinson’s disease, multiple sclerosis, or epilepsy. This single-center observational study involved patients (N = 197) and their treating physicians (N = 12) answering questionnaires (18-item Patient Satisfaction Questionnaire Short Form, 9-item Shared Decision Making Questionnaire for patients and physicians, Barthel Index, and 36-item Short Form subdomains) over two clinic visits. The primary outcome was the difference between pairwise items in the questionnaires (perception gap). Perception gaps, albeit minimal, were identified for patient satisfaction, shared decision-making, activities of daily living, and quality of life. Attributes that significantly influenced perception gaps included physician’s age, years of experience/holding a neurologist qualification, disease area, and the number of patients treated, with experienced physicians tending to provide more rigorous evaluations than their patients’ self-assessments. Multiple machine learning algorithms were used to develop predictive models based on study data. The k-nearest neighbors algorithm demonstrated the best performance in predicting a patient–physician perception gap. Insights from our study highlight the potential to recognize, predict, and ultimately address these gaps, thus enhancing clinical practice by increasing the level of understanding between patients and their physicians.

Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson’s disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor–derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51–1.44 for clinical assessments and 0.56–1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62–0.80 for scores derived from weekly averages and 0.24–0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings. Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.

Orthostatic hypotension (OH) frequently accompanies autonomic dysfunction and is an important risk factor for cognitive impairment in Parkinson's disease (PD). While OH is usually diagnosed based on an orthostatic blood pressure drop, the association between the heart rate response and cognitive impairment remains unclear. We retrospectively analyzed 143 cases of clinically diagnosed PD to determine the association between the absence of a heart rate response and cognitive impairment in PD with OH. Among the patients with OH, neurogenic OH was diagnosed in cases without a heart rate increase, while all other patients were diagnosed with non-neurogenic OH. Dementia was found in 23 of 143 PD cases (16.1%) in this cohort. The presence of OH was an independent risk factor for dementia in PD in addition to the disease severity, years of education and beta-blockers use. Neurogenic OH was significantly associated with dementia compared to the no OH group (hazard ratio [HR] 7.3, 95% confidence interval [CI] 2.2-24.6, P<0.01), an association that was preserved after adjusting for age, gender and other covariant factors. However, no such association was observed for non-neurogenic OH (HR 2.9, 95%CI 0.8-10.9, P = 0.12). While the cognitive impairment was significantly worse in the neurogenic OH group than the no-OH group, the groups were otherwise similar. The blood pressure decrease was significantly lower in both OH groups than in the no-OH group, despite no significant differences between the OH groups. Our finding showed that OH without a heart rate response was an important predictor of cognitive impairment in PD.

In super-aged societies, dementia has become a critical issue, underscoring the urgent need for tools to assess cognitive status effectively in various sectors, including financial and business settings. Facial and speech features have been tried as cost-effective biomarkers of dementia including Alzheimer’s disease (AD). We aimed to establish an easy, automatic, and extensive screening tool for AD using a chatbot and artificial intelligence. Smile images and visual and auditory data of natural conversations with a chatbot from 99 healthy controls (HCs) and 93 individuals with AD or mild cognitive impairment due to AD (PwA) were analyzed using machine learning. A subset of 8 facial and 21 sound features successfully distinguished PwA from HCs, with a high area under the receiver operating characteristic curve of 0.94 ± 0.05. Another subset of 8 facial and 20 sound features predicted the cognitive test scores, with a mean absolute error as low as 5.78 ± 0.08. These results were superior to those obtained from face or auditory data alone or from conventional image depiction tasks. Thus, by combining spontaneous sound and facial data obtained through conversations with a chatbot, the proposed model can be put to practical use in real-life scenarios.

Purpose This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson’s disease (PD) diagnosis. Methods NM-MRI datasets from two different 3T-scanners were used: a “principal dataset” with 122 participants and an “external validation dataset” with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined. Results For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets. Conclusion U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.

The objective of this study is to test the feasibility of a semi-automated scoring system for the Toronto Western Spasmodic Torticollis Scale (TWSTRS) severity scale in patients with cervical dystonia. The TWSTRS requires training and experience. We previously developed a system to measure neck angle by analyzing three-dimensional position, obtained using Kinect, a marker-less three-dimensional depth sensor. The system can track patients' faces and bodies, automatically analyze neck angles, and semi-automatically calculate the TWSTRS severity scale score. We compared the TWSTRS severity scale scores calculated by the system with the video-based scores calculated by a neurologist trained in movement disorders. A correlation coefficient analysis was then conducted. Absolute accuracy was measured using intra-class correlation (ICC) (3,1), with 95% limits of agreement. To analyze the subscales, Cohen's kappa coefficient (κ) was calculated. A p-value of < .05 was considered statistically significant. Thirty patients were enrolled. Their average age was 52.3±16.0 years, and the male to female ratio was 3:2. The average disease duration was 11.3±12.7 years. Total score measurements by the system were significantly correlated with those rated by the movement disorder-trained neurologist (r = .596, p < .05). There was a significant correlation (r = .655, p < .05) with regard to the automated part of the scale. An adequate ICC (3,1) of .562 was obtained for total severity score (p < .001, 95% confidence interval [CI]: .259-.765), while the equivalent score was .617 for the total automated part (p < .001, 95% CI .336-.798). Our three-dimensional motion capture system, which can measure head angles and semi-automatically calculate the TWSTRS severity scale score utilizing a single-depth camera, demonstrated adequate validity and reliability. This low-cost and portable system could be applied by general practitioners treating cervical dystonia to obtain objective measurements.